Last Updated on by Kumar Raja

Different Techniques For Data Mining In Data Science

Data Mining is one of the advanced concepts in Data Science that deals with the technique of analyzing hidden, valid, and potentially useful patterns inside Big Data. To explain this in a simple manner, the process of Data Mining is all about finding the unknown co-relations between the data patterns.

Data Mining is a multi-disciplinary skill that makes use of advanced techniques in Machine Learning, Statistics, & database technology to accurately interpret with Big Data of any form & sheer size. The application of Data Mining is very crucial for fraud detection, scientific discovery, marketing analysis in business & many more. Data Mining is a must to have skill in the inventory of a Data Scientist.

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There are several free tools available in the market that supports Data Mining operations. Now, let’s have a look at different Data Mining techniques.

  • Classification

Classification technique is very crucial when it comes to retrieving relevant information about data, and metadata. As the name itself suggest, classification technique helps in  the process of classifying data in different classes.

  • Clustering

Using clustering analysis data analysts can easily identify the different sets of data that are related to each other. Using clustering technique analysts can easily identify the similarities & differences in the data between the data sets.

  • Regression

When it comes to identifying the relation between different set of variables then the best technique in use is Regression analysis. Using this Data Mining technique, analytics can easily identify the likelihood of a specific variable, given the presence of other variables.

  • Association Rules

When it comes to finding the association between multiple variable items then the best Data Mining technique in use is Association Rule. This technique is also used to uncover the hidden patterns in the data sets.

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